Bibliographic record
Abstract
Considering the development of artificial intelligence (AI) in various fields, especially the closeness of its function to the human brain in terms of perception and the understanding of sensory and emotional concepts, it can be concluded that this concept is cognitively evolving toward an artificial mind (AM). This article introduces the concept of AM as a more accurate interpretation of the future of AI. It explores the distinction between intelligence and mind, highlighting the holistic nature of the mind, which includes cognitive, psychological, and emotional dimensions. Various types of intelligence, from rational to emotional, are categorized to emphasize their role in shaping human abilities. The study evaluates the human mind, focusing on cognitive functions, logical thinking, emotional understanding, learning, and creativity. It encourages AI systems to understand contextual, emotional, and subjective aspects and aligns AI with human intelligence through advanced perception and emotional capabilities. The shift from AI to AM has significant implications, transforming work, education, and human–machine collaboration, and promises a future where AI systems integrate advanced perceptual and emotional functions. This narrative guides the conversation around AI terminology, emphasizing the convergence of artificial and human intelligence and acknowledging the social implications. Therefore, the termAMappears to be a more appropriate term thanAI, symbolizing the transformative technological change and its multifaceted impact on society.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.047 |
| Scholarly communication | 0.016 | 0.023 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.009 | 0.018 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".